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PMID: 18595727 Published · ppublish English Journal Article Review

Serum and tissue biomarkers as predictive and prognostic variables in epithelial ovarian cancer.

Critical reviews in oncology/hematology ·Vol. 69 ·No. 1 ·2009-01-00 ·Pages 12-27

Gadducci A, Cosio S, Tana R, Genazzani AR

Abstract

Tumour stage, residual disease after initial surgery, histological type and tumour grade are the most important clinical-pathological factors related to the clinical outcome of patients with epithelial ovarian cancer. In the last years, several investigations have assessed different biological variables in sera and in tissue samples from patients with this malignancy in order to detect biomarkers able to reflect either the response to chemotherapy or survival. The present paper reviewed the literature data about the predictive or prognostic relevance of serum CA 125, soluble cytokeratin fragments, serum human kallikreins, serum cytokines, serum vascular endothelial growth factor and plasma d-dimer as well as of tissue expression of cell cycle- and apoptosis-regulatory proteins, human telomerase reverse transcriptase, membrane tyrosine kinase receptors and matrix metalloproteinases. A next future microarray technology will hopefully offer interesting perspectives of translational research for the identification of novel predictive and prognostic biomarkers for epithelial ovarian cancer.

MeSH Terms
Biomarkers, Tumor/analysis,blood Carcinoma/blood,chemistry,diagnosis,drug therapy Female Humans Ovarian Neoplasms/blood,chemistry,diagnosis,drug therapy Prognosis
Chemicals
Biomarkers, Tumor
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Gadducci Angiolo
Department of Procreative Medicine, Division of Gynecology and Obstetrics, University of Pisa, Via Roma 56, Pisa 56127, Italy. a.gadducci@obgyn.med.unipi.it
Cosio Stefania
Tana Roberta
Genazzani Andrea Riccardo
Article Info
Journal
Critical reviews in oncology/hematology
Abbr.
Crit Rev Oncol Hematol
ISSN
1879-0461
Published
2009-01-00
Epub
2008-00-01
Pages
12-27
Language
English
Region
Netherlands
NLM ID
8916049
Subset
IM
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